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Cognitive Computing of Alzheimer's Disease Genes and Risk

Award Number: U01AG068214
ORGANIZATION: NATIONAL INSTITUTE ON AGING
OPDIV: NIH
AWARD CLASS: COOPERATIVE AGREEMENT
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 07/01/2021
PERIOD OF PERFORMANCE END DATE: 06/30/2026

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Cognitive Computing of Alzheimer's Disease Genes and Risk - Cognitive Computing of Alzheimer’s Disease Genes and Risk The molecular basis and genetic architecture of dementia remain a puzzle. As no drug yet prevents, delays, or reverses it, aging populations potentially face a tidal threat of incipient and socially disruptive Alzheimer’s Disease (AD) cases. Genome-wide association studies (GWAS) have linked over 100 loci with AD and explain much of population attributable risk, but only a fraction of heritability. This heritability gap means it remains difficult to design and assess which surveillance, screening, preventive, and stratification programs are effective. In turn, this hinders therapeutic trials. The challenge in translating genetic variants into patient classifications is twofold. First, AD is polygenic, so relevant disease driving mutations are spread thin across a multitude of different genes and patients. Second, current interpretations of the deleterious effects of mutations lack accuracy, so the impactful few cannot be distinguished from the benign multitude in any given subject. These problems compound and fog the statistical genetics of AD risk and morbidity with poor signal to noise ratio. The crux of our solution is to add a massive amount of new information, exploit it efficiently through computation, then perform rigorous multi-pronged experimental validation. We start from the hypothesis that AD arises through mutational perturbations that affect functional pathways beyond the built-in evolutionary tolerances. New algorithms compute these excessive mutational forces and place them in integrative machine learning frameworks to sort between AD patients and controls, and which can also reflect functional interactions among proteins or genes. Innovations include a mathematical model of evolution based on calculus; ensemble machine learning over human genome variations; and harmonic analysis of mutational perturbations in functional networks. The outcome will, for the first time, integrate genomic variations relevant to AD in the context of all relevant evolutionary history and all known functional interactions. In practice, this will increase power and resolution, enable gender-specific analysis and AD stratification of men and women, and identify new and experimentally validated AD genes. To carry out this program, AIM 1 will fuse a novel mathematical analysis of evolution with machine learning and network wavelet theory. This will yield complementary integrative approaches to identify genes and mutations that sort AD vs healthy subjects based on the abnormal mutational burden of rare gene variants in sequenced cohorts. AIM 2 will focus similar tools on patients and controls with known paradoxical phenotypes that run counter to their APOEɛ2/4 status. The results will identify modifier genes that drive AD in APOEɛ2 carriers or that protect APOEɛ4 carriers from AD. AIM 3 will provide direct experimental validation, leveraging high-throughput, robot-assisted genetic modifier screening in Drosophila models of Tau or amyloid-beta peptide neurotoxicity. Promising targets will be further confirmed in mammalian neuronal cell culture. The work will validate a new approach to enlarge our understanding of genetic complexity in Alzheimer’s Disease for the identification of gene drivers and modifiers to guide clinical assessment of AD risk stratification.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $0 )
2026	2025	BAYLOR COLLEGE OF MEDICINE	1 BAYLOR PLZ	HOUSTON	TX	77030	HARRIS	USA	Aging Research	000	5	12/8/2025	NON-COMPETING CONTINUATION	$0
														Subtotal = $0

Issue Date FY: 2025 ( Subtotal = $783,998 )
2025	2025	BAYLOR COLLEGE OF MEDICINE	1 BAYLOR PLZ	HOUSTON	TX	77030	HARRIS	USA	Aging Research	000	5	7/31/2025	NON-COMPETING CONTINUATION	$783,998
														Subtotal = $783,998

Issue Date FY: 2024 ( Subtotal = $799,998 )
2024	2024	BAYLOR COLLEGE OF MEDICINE	1 BAYLOR PLZ	HOUSTON	TX	77030	HARRIS	USA	Aging Research	000	4	8/13/2024	NON-COMPETING CONTINUATION	$799,998
														Subtotal = $799,998

Issue Date FY: 2023 ( Subtotal = $1,247,322 )
2023	2023	BAYLOR COLLEGE OF MEDICINE	1 BAYLOR PLZ	HOUSTON	TX	77030	HARRIS	USA	Aging Research	001	3	7/10/2023	NON-COMPETING CONTINUATION	$799,998
2023	2023	BAYLOR COLLEGE OF MEDICINE	1 BAYLOR PLZ	HOUSTON	TX	77030	HARRIS	USA	Aging Research	003	3	8/25/2023	SUPPLEMENT FOR EXPANSION	$271,143
2023	2023	BAYLOR COLLEGE OF MEDICINE	1 BAYLOR PLZ	HOUSTON	TX	77030	HARRIS	USA	Aging Research	002	3	8/3/2023	SUPPLEMENT FOR EXPANSION	$176,181
2023	2022	BAYLOR COLLEGE OF MEDICINE	1 BAYLOR PLZ	HOUSTON	TX	77030	HARRIS	USA	Aging Research	000	2	2/16/2023	NON-COMPETING CONTINUATION	$0
														Subtotal = $1,247,322

Issue Date FY: 2022 ( Subtotal = $799,998 )
2022	2022	BAYLOR COLLEGE OF MEDICINE	1 BAYLOR PLZ	HOUSTON	TX	77030	HARRIS	USA	Aging Research	000	2	7/21/2022	NON-COMPETING CONTINUATION	$799,998
														Subtotal = $799,998

Issue Date FY: 2021 ( Subtotal = $799,998 )
2021	2021	BAYLOR COLLEGE OF MEDICINE	ONE BAYLOR PLAZA	HOUSTON	TX	77030	HARRIS	USA	Aging Research	000	1	6/23/2021	NEW	$799,998
														Subtotal = $799,998

Grand Total All Awards = $4,431,314

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Cognitive Computing of Alzheimer's Disease Genes and Risk

Award Number: U01AG068214

ORGANIZATION: NATIONAL INSTITUTE ON AGING

OPDIV: NIH

AWARD CLASS: COOPERATIVE AGREEMENT

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 07/01/2021

PERIOD OF PERFORMANCE END DATE: 06/30/2026

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